Robust asymmetric Adaboost

Pablo Ormeño, Felipe Ramírez, Carlos Valle, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In real world pattern recognition problems, such as computer-assisted medical diagnosis, events of a given phenomena are usually found in minority, making it necessary to build algorithms that emphasize the effect of one of the classes at training time. In this paper we propose a variation of the well-known Adaboost algorithm that is able to improve its performance by using an asymmetric and robust cost function. We assess the performance of the proposed method on two medical datasets and synthetic datasets with different levels of imbalance and compare our results against three state-of-the-art ensemble learning approaches, achieving better and comparable results.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 17th Iberoamerican Congress, CIARP 2012, Proceedings
Pages519-526
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 - Buenos Aires, Argentina
Duration: 3 Sep 20126 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7441 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
CountryArgentina
CityBuenos Aires
Period3/09/126/09/12

Keywords

  • adaboost
  • asymmetric cost functions
  • ensemble learning
  • robust methods

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